Consulting Services

Indigenous data sovereignty, AI, and data science consulting

I’m available for consulting and contract work. I’m an enrolled member of the Chippewa Cree Tribe of the Rocky Boy Reservation in Montana and a descendant of the Salish, Kootenai, and Pend d’Oreille Tribes of the Flathead Reservation. I earned my degree at Salish Kootenai College, a tribal college, and I bring 10+ years of building data, scientific computing, and AI systems at the National Institutes of Health. Here’s what I can help with. If your project doesn’t fit neatly into one of these, email me anyway.

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Indigenous Data Sovereignty

Indigenous data sovereignty (IDSov) is the inherent right of Tribal Nations to govern the collection, ownership, and use of data about their people, lands, and resources. For too long, data about Native communities, including my own, has been collected about us rather than by or for us, and it has often been framed around deficits and used without consent. I help Tribal Nations, Tribal colleges, Native-serving organizations, and their research partners put sovereignty into practice, both in policy and in the technical systems that hold the data.

My work is grounded in the frameworks Indigenous communities have built themselves: the CARE Principles for Indigenous Data Governance, the First Nations Principles of OCAP®, UNDRIP, and Local Contexts Notices and Labels.

For Tribal Nations and Native organizations

Data governance strategy. Take inventory of the data your Nation holds and what others hold about you, then build a governance plan for collecting, storing, accessing, and sharing it.
Policies, codes, and data sharing agreements. Draft data governance policies, research codes, and sovereign data sharing agreements that set clear limits on how outside partners create, use, and exchange your data. I work alongside your legal counsel and leadership; I’m not a lawyer.
Tribal research review. Help set up or strengthen a Tribal Institutional Review Board (IRB) or research review process, including review criteria, data management plans, and consent practices that cover the whole data lifecycle.
AI governance. Write generative AI use policies that protect personal, health, and cultural information, including rules for what can and can’t go into public AI tools. I can also assess which AI tools are appropriate for your community.
Tribally controlled infrastructure. Design and build data systems your Nation physically possesses and controls: self-hosted databases, dashboards, secure cloud environments, and private, open source LLM tools that keep sensitive data out of third-party hands and avoid vendor lock-in.
Capacity building and training. Workshops on Indigenous data sovereignty, data literacy, and practical data skills for Tribal staff, councils, and students, so the expertise stays in the community.

For researchers, universities, and agencies working with Tribes

FAIR and CARE alignment. Make your data management practices both FAIR (findable, accessible, interoperable, reusable) and CARE-compliant, so open science doesn’t come at the expense of Tribal sovereignty.
Partnership and data management plans. Review research proposals, data management and sharing plans, and agreements to make sure Tribal partners keep authority over their data from collection through publication.
Local Contexts implementation. Add Local Contexts Notices and Labels to your repositories, collections, and metadata so provenance, protocols, and permissions travel with the data.
Training for research teams. An introduction to Indigenous data sovereignty for researchers, data managers, and IRB staff, covering the history, the frameworks, and what changes in day-to-day practice.

AI and LLM Systems

LLM strategy and evaluation. Figure out where LLMs actually help your organization, choose between hosted and self-hosted models, and evaluate them with tools like LangFuse and promptfoo before you commit.
Retrieval augmented generation (RAG). Build search and question-answering systems over your documents, using embeddings and vector databases (Chroma, FAISS, pgvector) and knowledge graphs.
Agentic workflows and data extraction. Use LangChain, LangGraph, and LlamaIndex to pull structured data out of messy, unstructured sources.
Private LLM deployments. Stand up internal LLM interfaces and APIs built on open source models, as I did at NIEHS, so your data stays in your environment.

Cloud, Data, and Scientific Computing

Cloud infrastructure and MLOps. Secure, repeatable AWS environments built with infrastructure as code, CI/CD, and containers, including compliance-focused migrations like the NIH-compliant move I led for the CAMERA platform.
Data science platforms. Set up and run Posit Team (Connect, Workbench, Package Manager) so your analysts can publish Quarto, R Markdown, and Jupyter reports, Shiny apps, and APIs on their own.
Reproducible analysis and reporting. Pipelines built with targets, renv, Docker, and Quarto that can be rerun years later, plus polished reports, dashboards, and websites.
Scientific and environmental health data. Toxicogenomics, dose-response analysis (BMDExpress), network analysis with Cytoscape, and HPC workflows on Slurm clusters.

Training and Developer Enablement

I’ve onboarded and taught scientific developers for years, through workshops, one-on-one coaching, and the NIEHS Scientific Developer’s Guide. I can run workshops or write documentation on Git and GitHub, reproducible research, Quarto, R and Python tooling, HPC, Cytoscape, and practical LLM use.

How I work

Start with a conversation. We talk through what you need, and I’ll tell you honestly whether I’m the right fit.
Flexible engagements. One-time workshops, short assessments, ongoing advising, or hands-on build projects. Remote by default, based in Durham, NC.
Your data stays yours. I follow your data protocols, sign whatever data agreements you require, and leave behind documentation and training so you aren’t dependent on me.

Ready to talk? Email me at treyosaddler@gmail.com.